Text generation web UI
A gradio web UI for running Large Language Models like LLaMA, llama.cpp, GPT-J, OPT, and GALACTICA.
Its goal is to become the AUTOMATIC1111/stable-diffusion-webui of text generation.
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Features
- 3 interface modes: default, notebook, and chat
- Multiple model backends: transformers, llama.cpp, ExLlama, AutoGPTQ, GPTQ-for-LLaMa
- Dropdown menu for quickly switching between different models
- LoRA: load and unload LoRAs on the fly, train a new LoRA
- Precise instruction templates for chat mode, including Llama 2, Alpaca, Vicuna, WizardLM, StableLM, and many others
- Multimodal pipelines, including LLaVA and MiniGPT-4
- 8-bit and 4-bit inference through bitsandbytes
- CPU mode for transformers models
- DeepSpeed ZeRO-3 inference
- Extensions
- Custom chat characters
- Very efficient text streaming
- Markdown output with LaTeX rendering, to use for instance with GALACTICA
- Nice HTML output for GPT-4chan
- API, including endpoints for websocket streaming (see the examples)
To learn how to use the various features, check out the Documentation: https://github.com/oobabooga/text-generation-webui/tree/main/docs
Installation
One-click installers
| Windows | Linux | macOS | WSL |
|---|---|---|---|
| oobabooga-windows.zip | oobabooga-linux.zip | oobabooga-macos.zip | oobabooga-wsl.zip |
Just download the zip above, extract it, and double-click on "start". The web UI and all its dependencies will be installed in the same folder.
- The source codes are here: https://github.com/oobabooga/one-click-installers
- There is no need to run the installers as admin.
- AMD doesn't work on Windows.
- Huge thanks to @jllllll, @ClayShoaf, and @xNul for their contributions to these installers.
Manual installation using Conda
Recommended if you have some experience with the command line.
0. Install Conda
https://docs.conda.io/en/latest/miniconda.html
On Linux or WSL, it can be automatically installed with these two commands:
curl -sL "https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh" > "Miniconda3.sh"
bash Miniconda3.sh
Source: https://educe-ubc.github.io/conda.html
1. Create a new conda environment
conda create -n textgen python=3.10.9
conda activate textgen
2. Install Pytorch
| System | GPU | Command |
|---|---|---|
| Linux/WSL | NVIDIA | pip3 install torch torchvision torchaudio |
| Linux/WSL | CPU only | pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu |
| Linux | AMD | pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.4.2 |
| MacOS + MPS | Any | pip3 install torch torchvision torchaudio |
| Windows | NVIDIA | pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117 |
| Windows | CPU only | pip3 install torch torchvision torchaudio |
The up-to-date commands can be found here: https://pytorch.org/get-started/locally/.
2.1 Special instructions
- MacOS users: oobabooga#393
- AMD users: https://rentry.org/eq3hg
3. Install the web UI
git clone https://github.com/oobabooga/text-generation-webui
cd text-generation-webui
pip install -r requirements.txt
bitsandbytes
bitsandbytes >= 0.39 may not work on older NVIDIA GPUs. In that case, to use --load-in-8bit, you may have to downgrade like this:
- Linux:
pip install bitsandbytes==0.38.1 - Windows:
pip install https://github.com/jllllll/bitsandbytes-windows-webui/raw/main/bitsandbytes-0.38.1-py3-none-any.whl
Alternative: Docker
ln -s docker/{Dockerfile,docker-compose.yml,.dockerignore} .
cp docker/.env.example .env
# Edit .env and set TORCH_CUDA_ARCH_LIST based on your GPU model
docker compose up --build
- You need to have docker compose v2.17 or higher installed. See this guide for instructions.
- For additional docker files, check out this repository.
Updating the requirements
From time to time, the requirements.txt changes. To update, use this command:
conda activate textgen
cd text-generation-webui
pip install -r requirements.txt --upgrade
Downloading models
Models should be placed inside the models/ folder.
Hugging Face is the main place to download models. These are some examples:
You can automatically download a model from HF using the script download-model.py:
python download-model.py organization/model
For example:
python download-model.py facebook/opt-1.3b
To download a protected model, set env vars HF_USER and HF_PASS to your Hugging Face username and password (or User Access Token). The model's terms must first be accepted on the HF website.
GGML models
You can drop these directly into the models/ folder, making sure that the file name contains ggml somewhere and ends in .bin.
Starting the web UI
conda activate textgen
cd text-generation-webui
python server.py
Then browse to
http://localhost:7860/?__theme=dark
Optionally, you can use the following command-line flags:
Basic settings
| Flag | Description |
|---|---|
-h, --help |
Show this help message and exit. |
--notebook |
Launch the web UI in notebook mode, where the output is written to the same text box as the input. |
--chat |
Launch the web UI in chat mode. |
--multi-user |
Multi-user mode. Chat histories are not saved or automatically loaded. WARNING: this is highly experimental. |
--character CHARACTER |
The name of the character to load in chat mode by default. |
--model MODEL |
Name of the model to load by default. |
--lora LORA [LORA ...] |
The list of LoRAs to load. If you want to load more than one LoRA, write the names separated by spaces. |
--model-dir MODEL_DIR |
Path to directory with all the models. |
--lora-dir LORA_DIR |
Path to directory with all the loras. |
--model-menu |
Show a model menu in the terminal when the web UI is first launched. |
--no-stream |
Don't stream the text output in real time. |
--settings SETTINGS_FILE |
Load the default interface settings from this yaml file. See settings-template.yaml for an example. If you create a file called settings.yaml, this file will be loaded by default without the need to use the --settings flag. |
--extensions EXTENSIONS [EXTENSIONS ...] |
The list of extensions to load. If you want to load more than one extension, write the names separated by spaces. |
--verbose |
Print the prompts to the terminal. |
Model loader
| Flag | Description |
|---|---|
--loader LOADER |
Choose the model loader manually, otherwise, it will get autodetected. Valid options: transformers, autogptq, gptq-for-llama, exllama, exllama_hf, llamacpp, rwkv |
Accelerate/transformers
| Flag | Description |
|---|---|
--cpu |
Use the CPU to generate text. Warning: Training on CPU is extremely slow. |
--auto-devices |
Automatically split the model across the available GPU(s) and CPU. |
--gpu-memory GPU_MEMORY [GPU_MEMORY ...] |
Maximum GPU memory in GiB to be allocated per GPU. Example: --gpu-memory 10 for a single GPU, --gpu-memory 10 5 for two GPUs. You can also set values in MiB like --gpu-memory 3500MiB. |
--cpu-memory CPU_MEMORY |
Maximum CPU memory in GiB to allocate for offloaded weights. Same as above. |
--disk |
If the model is too large for your GPU(s) and CPU combined, send the remaining layers to the disk. |
--disk-cache-dir DISK_CACHE_DIR |
Directory to save the disk cache to. Defaults to cache/. |
--load-in-8bit |
Load the model with 8-bit precision (using bitsandbytes). |
--bf16 |
Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU. |
--no-cache |
Set use_cache to False while generating text. This reduces the VRAM usage a bit with a performance cost. |
--xformers |
Use xformer's memory efficient attention. This should increase your tokens/s. |
--sdp-attention |
Use torch 2.0's sdp attention. |
--trust-remote-code |
Set trust_remote_code=True while loading a model. Necessary for ChatGLM and Falcon. |
Accelerate 4-bit
| Flag | Description |
|---|---|
--load-in-4bit |
Load the model with 4-bit precision (using bitsandbytes). |
--compute_dtype COMPUTE_DTYPE |
compute dtype for 4-bit. Valid options: bfloat16, float16, float32. |
--quant_type QUANT_TYPE |
quant_type for 4-bit. Valid options: nf4, fp4. |
--use_double_quant |
use_double_quant for 4-bit. |
llama.cpp
| Flag | Description |
|---|---|
--threads |
Number of threads to use. |
--n_batch |
Maximum number of prompt tokens to batch together when calling llama_eval. |
--no-mmap |
Prevent mmap from being used. |
--mlock |
Force the system to keep the model in RAM. |
--cache-capacity CACHE_CAPACITY |
Maximum cache capacity. Examples: 2000MiB, 2GiB. When provided without units, bytes will be assumed. |
--n-gpu-layers N_GPU_LAYERS |
Number of layers to offload to the GPU. Only works if llama-cpp-python was compiled with BLAS. Set this to 1000000000 to offload all layers to the GPU. |
--n_ctx N_CTX |
Size of the prompt context. |
--llama_cpp_seed SEED |
Seed for llama-cpp models. Default 0 (random). |
--n_gqa N_GQA |
grouped-query attention. Must be 8 for llama-2 70b. |
--rms_norm_eps RMS_NORM_EPS |
5e-6 is a good value for llama-2 models. |
--cpu |
Use the CPU version of llama-cpp-python instead of the GPU-accelerated version. |
AutoGPTQ
| Flag | Description |
|---|---|
--triton |
Use triton. |
--no_inject_fused_attention |
Disable the use of fused attention, which will use less VRAM at the cost of slower inference. |
--no_inject_fused_mlp |
Triton mode only: disable the use of fused MLP, which will use less VRAM at the cost of slower inference. |
--no_use_cuda_fp16 |
This can make models faster on some systems. |
--desc_act |
For models that don't have a quantize_config.json, this parameter is used to define whether to set desc_act or not in BaseQuantizeConfig. |
ExLlama
| Flag | Description |
|---|---|
--gpu-split |
Comma-separated list of VRAM (in GB) to use per GPU device for model layers, e.g. 20,7,7 |
--max_seq_len MAX_SEQ_LEN |
Maximum sequence length. |
GPTQ-for-LLaMa
| Flag | Description |
|---|---|
--wbits WBITS |
Load a pre-quantized model with specified precision in bits. 2, 3, 4 and 8 are supported. |
--model_type MODEL_TYPE |
Model type of pre-quantized model. Currently LLaMA, OPT, and GPT-J are supported. |
--groupsize GROUPSIZE |
Group size. |
--pre_layer PRE_LAYER [PRE_LAYER ...] |
The number of layers to allocate to the GPU. Setting this parameter enables CPU offloading for 4-bit models. For multi-gpu, write the numbers separated by spaces, eg --pre_layer 30 60. |
--checkpoint CHECKPOINT |
The path to the quantized checkpoint file. If not specified, it will be automatically detected. |
--monkey-patch |
Apply the monkey patch for using LoRAs with quantized models. |
DeepSpeed
| Flag | Description |
|---|---|
--deepspeed |
Enable the use of DeepSpeed ZeRO-3 for inference via the Transformers integration. |
--nvme-offload-dir NVME_OFFLOAD_DIR |
DeepSpeed: Directory to use for ZeRO-3 NVME offloading. |
--local_rank LOCAL_RANK |
DeepSpeed: Optional argument for distributed setups. |
RWKV
| Flag | Description |
|---|---|
--rwkv-strategy RWKV_STRATEGY |
RWKV: The strategy to use while loading the model. Examples: "cpu fp32", "cuda fp16", "cuda fp16i8". |
--rwkv-cuda-on |
RWKV: Compile the CUDA kernel for better performance. |
RoPE (for llama.cpp, ExLlama, and transformers)
| Flag | Description |
|---|---|
--alpha_value ALPHA_VALUE |
Positional embeddings alpha factor for NTK RoPE scaling. Use either this or compress_pos_emb, not both. |
--compress_pos_emb COMPRESS_POS_EMB |
Positional embeddings compression factor. Should typically be set to max_seq_len / 2048. |
Gradio
| Flag | Description |
|---|---|
--listen |
Make the web UI reachable from your local network. |
--listen-host LISTEN_HOST |
The hostname that the server will use. |
--listen-port LISTEN_PORT |
The listening port that the server will use. |
--share |
Create a public URL. This is useful for running the web UI on Google Colab or similar. |
--auto-launch |
Open the web UI in the default browser upon launch. |
--gradio-auth USER:PWD |
set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3" |
--gradio-auth-path GRADIO_AUTH_PATH |
Set the gradio authentication file path. The file should contain one or more user:password pairs in this format: "u1:p1,u2:p2,u3:p3" |
--ssl-keyfile SSL_KEYFILE |
The path to the SSL certificate key file. |
--ssl-certfile SSL_CERTFILE |
The path to the SSL certificate cert file. |
API
| Flag | Description |
|---|---|
--api |
Enable the API extension. |
--public-api |
Create a public URL for the API using Cloudfare. |
--public-api-id PUBLIC_API_ID |
Tunnel ID for named Cloudflare Tunnel. Use together with public-api option. |
--api-blocking-port BLOCKING_PORT |
The listening port for the blocking API. |
--api-streaming-port STREAMING_PORT |
The listening port for the streaming API. |
Multimodal
| Flag | Description |
|---|---|
--multimodal-pipeline PIPELINE |
The multimodal pipeline to use. Examples: llava-7b, llava-13b. |
Presets
Inference settings presets can be created under presets/ as yaml files. These files are detected automatically at startup.
The presets that are included by default are the result of a contest that received 7215 votes. More details can be found here.
Contributing
If you would like to contribute to the project, check out the Contributing guidelines.
Community
- Subreddit: https://www.reddit.com/r/oobaboogazz/
- Discord: https://discord.gg/jwZCF2dPQN